The present invention relates to a method for Signal to Noise Ratio (SNR) estimation in a communication system and, more particularly, but not exclusively to a method for SNR estimation in a wireless communication system which uses Adaptive Coding and Modulation (ACM).
Wireless communication links are known to be sensitive to time-varying propagation conditions.
Wireless communication links may have multiple propagation paths with different attenuation and delay characteristics, and produce an effect known as fading. Millimeter wave communication links may be affected by fading caused by rain attenuation. In some communication links co-channel and adjacent-channel signals introduce interference which degrades quality of a received signal. These phenomena, which are time-varying, reduce the Signal to Noise Ratio (SNR) which is produced at a receiver output.
Channel capacity is a term used for a mathematical measure of a data rate which may be transmitted over a given channel with no errors. The channel capacity is proportional to a logarithm of the channel SNR. An SNR increment of 3 dB increases the channel capacity by approximately 1 bit per Hz. As described above, channel capacity is time-varying.
Some modern communication systems try to maximize communication rate by transmitting at a data rate which tracks the time-varying channel capacity. For this purpose a channel SNR estimator is typically included in the communication systems. The modulation type and coding rate of the communication system are typically selected from a predetermined set according to the estimated SNR, and possibly according to adaptive SNR threshold levels. Such systems are referred to herein as Adaptive Coding and Modulation (ACM) systems or Adaptive Modulation and Coding (AMC) systems.
Typical operation of an ACM system is as follows. A receiver estimates the channel SNR as an indication of communication quality. If the quality is sufficiently high that upgrading of the communication data rate is possible, the receiver sends a request for an upgraded coding/modulation scheme to the transmitter on a return link. A similar procedure takes place when the communication quality nears a threshold of operation in which intolerable errors may occur. In such a case, the transmission switches to a lower communication rate.
If a receiver does not estimate SNR correctly and rapidly, reflecting channel quality changes, communication quality suffers in at least the following ways. If SNR is estimated too high, transmission may continue at its previous rate, and a receiver may completely lose ability to decode, leading to complete breakdown of communication. If SNR is estimated too low, the communication system uses a too conservative coding/modulation scheme, losing rate relative to what could actually be used. Slowly, the conservative coding/modulation scheme is upgraded because the communication link SNR is actually higher than initially estimated. SNR estimation is a common problem in miscellaneous systems and particularly in communication systems. A received signal contains a transmitted signal with additive noise. A ratio between signal power and noise power is an important measure of channel quality. Measurement of SNR is required for various applications such as adaptive coding, adaptive modulations, error correcting code, dynamic PLL bandwidth etc.
The SNR measurement problem is usually simple when signal and noise can be separated. In such a case SNR is estimated using a Mean Square Error (MSE) estimator. An MSE estimator is a decision directed, or data aided, estimator which takes a difference between a received signal and symbols decoded from the received signal, and calculates the mean square error. Such an estimator can be efficient when transmitted symbols are discrete and the receiver is phase-locked.
However, in low SNR, when the receiver is not phase-locked, the above method cannot estimate SNR accurately, because the decisions are not reliable.
Therefore, for low SNR, a non-data aided (NDA) estimator is needed. An NDA estimator is an estimator that estimates SNR without knowledge of the actual transmitted data.
The following are some references which describe SNR estimation.
U.S. Pat. No. 6,760,370 to Li at el. teaches a method for estimating signal-to-noise ratio (SNR) using a method with low bias that is effective for both positive SNRs and small to negative SNRs. The method is based on an iterative solution for the maximum likelihood estimate of the amplitude from which the SNR can be computed. The method is applicable for various modulated systems, including BPSK, QPSK and MPSK.
U.S. Pat. No. 6,611,794 to Fleming-Dahl teaches an apparatus for signal amplitude restoration having a received signal input and a scaled received signal output. An amplitude correction factor generator has an estimated signal-to-noise power ratio input and a received signal input. A variable gain amplifier uses the correction factor generator output to control its gain, and amplifies or attenuates the received signal input to provide the scaled received signal output. An average SNR estimator uses the amplifier output as its input, and provides an output connected to the estimated signal-to-noise power ratio input. The apparatus processes received signals in an iterative fashion, such that at least one of the outputs is stored for use as a feedback input during later iterations.
US Published Patent Application 2004/0264588 of Song et al. teaches a method and device for adaptive modulation and coding based on second order statistics of channel information in OFDM system, characterized in that, by means of variance of Signal-to-Noise ratio (SNR) an appropriate adaptation time window is selected dynamically to trace time-varying channel better; and in that a decision criterion of second order, namely selecting an appropriate modulation and coding schemes (MCS) according to average value of SNR and variance of SNR, is employed to obtain accurate mapping from SNR to MCS. The mapping enhances practicability of the adaptive modulation and coding, decreases probability of system outage, and thus results in better performance of bit error rate.
US Published Patent Application 2004/0081259 of Ammer et al. teaches a receiver for iterative decoding of a received, encoded signal that employs slot-based scaling of soft samples. Iterative decoding employs a constituent maximum a priori (MAP) decoder for each constituent encoding of information of the encoded signal. Root mean square (RMS) values for soft samples over a slot are selected for dynamic range scaling. Squared RMS values are combined and equal the squared RMS value for a frame multiplied by a control constant, and this relationship may be employed to derive scaling constants for each slot. Alternatively, the square root of the RMS value multiplied by a constant serves as an SNR estimator that may be employed to scale samples to reduce dynamic range and modify logarithmic correction values for max* term calculation during log-MAP decoding.
US Published Patent Application 2005/0169391 of Takeda et al. teaches a radio communications system for performing communications based on an adaptive modulation by selecting one MCS from a set of MCSs each comprising a combination of a modulation scheme and a coding scheme which are ranked according to a transmission rate, the radio communications system comprising a change unit to change the selected MCS to a MCS of a higher ranking than the selected MCS when communication quality exceeds a first threshold, and change the selected MCS to a MCS of a lower ranking than the selected MCS when the communication quality is less than a second threshold lower than the first threshold, a first threshold controller to control the first threshold based on a first error rate, and a second threshold controller to control the second threshold based on a second error rate different from the first error rate.
An article titled “The joint estimation of signal and noise from the sum envelope”, by Benedict and Soong, published in IEEE Trans. Inform. Theory, vol. IT-13, no. 3, pp. 447-454, 1967, teaches computing the SNR from high order averages of an envelope of a modulated signal.
An article titled “SNR Estimation for non-Constant Modulus Constellations”, by Ping Gao and Cihan Tepedelenlioglu, published in IEEE Trans. Signal Processing vol. 53, no. 3, March 2005, pp. 865-870.
An article titled “Maximum likelihood from incomplete data via the EM algorithm”, by Dempster, Laird & Rubin, in the Journal of the Royal Statistical Society, Series B (Methodological), Vol. 39, No. 1 (1977), pp. 1-38.